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Advanced Wearable Sensors for Medical Applications
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Advanced Wearable Sensors for Medical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 13868

Special Issue Editors

Dr. Tomas Koutny

E-Mail Website
Guest Editor
Independent Researcher, 330 01 Pilsen, Czech Republic
Interests: in-silico modelling; artificial intelligence; high-performance computing; diabetes
Dr. Loris Nanni

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy
Interests: deep learning (ensembles of deep learners); medical image classification (general-purpose image classifiers, neonatal pain detection); biometrics systems (fingerprint classification and recognition, face recognition)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Personalized medicine conveys new, exciting challenges to our research when working with sensors, artificial intelligence, and wearable and smart devices to enhance the existing state of the art in personalized medicine. In recent years, we have observed great progress, as engineering has greatly contributed to the success of personalized medicine. Today, we employ a wide range of sensors and wearables to collect various data, which we can feed to artificial intelligence and in silico models. We are thus also able to create new and enhance existing diagnostics and therapeutics methods, which could be utilized to precisely target individual patients. However, this does not come for free. With new and improved methods, we see novel opportunities to improve the underlying models with extended datasets. This poses new challenges to sensors and wearables, and thus increases their capabilities. On the other hand, smart devices usually possess a low power, so that the computing power required for data processing must be limited or connected to the cloud. All these issues create manifold research challenges, and are therefore exiting to engage with.

This Special Issue will address topics related to the delivery of personalized medicine. These span sensory input to diagnostics methods, including, but not limited to, the following:

  • Data collection and process with wearables and smart sensors.
  • Application of artificial intelligence in medical sensors.
  • Intelligent sensors for personalized/precision medicine.
  • Image recognition and classification in medical sensing.

Dr. Tomas Koutny
Dr. Loris Nanni
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • personalized medicine
  • smart devices
  • wearables
  • imaging
  • artificial intelligence
  • deep learning
  • diagnostics

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Published Papers (6 papers)

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Research

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15 pages, 1633 KiB  
Article
Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals
by Zixiang Cai, Mengyao Qu, Mingyang Han, Zhijing Wu, Tong Wu, Mengtong Liu and Hailong Yu
Sensors 2025, 25(1), 13; https://doi.org/10.3390/s25010013 - 24 Dec 2024
Viewed by 904
Abstract
This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle [...] Read more.
This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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10 pages, 1055 KiB  
Article
Is an Ambulatory Biofeedback Device More Effective than Instructing Partial Weight-Bearing Using a Bathroom Scale? Results of a Randomized Controlled Trial with Healthy Subjects
by Tobias Peter Merkle, Nina Hofmann, Christian Knop and Tomas Da Silva
Sensors 2024, 24(19), 6443; https://doi.org/10.3390/s24196443 - 5 Oct 2024
Cited by 1 | Viewed by 1113
Abstract
So far, there have been no high-quality studies examining the efficacy of outpatient biofeedback devices in cases of prescribed partial weight-bearing, such as after surgery on the lower limbs. This study aimed to assess whether a biofeedback device is more effective than using [...] Read more.
So far, there have been no high-quality studies examining the efficacy of outpatient biofeedback devices in cases of prescribed partial weight-bearing, such as after surgery on the lower limbs. This study aimed to assess whether a biofeedback device is more effective than using a personal scale. Two groups of healthy individuals wearing an insole orthosis were trained to achieve partial loading in a three-point gait within a target zone of 15–30 kg during overground walking and going up and down stairs. The treatment group (20 women and 22 men) received continuous biofeedback, while the control group (26 women and 16 men) received no information. Findings were compared in a randomized controlled trial. Compliance with partial loading without biofeedback was poor; on level ground and stairs, only one in two steps fell within the target area, and overloading occurred on at least one in three steps. The treatment group reduced the percentage of steps taken in the overload zone to ≤8.4% (p < 0.001 across all three courses) and achieved more than two-thirds of their steps within the target zone (p < 0.001 on level ground, p = 0.008 upstairs, and p = 0.028 downstairs). In contrast, the control group did not demonstrate any significant differences in the target zone (p = 0.571 on level ground, p = 0.332 upstairs, and p = 0.392 downstairs). In terms of maintaining partial load, outpatient biofeedback systems outperform bathroom scales. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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14 pages, 5897 KiB  
Article
Validity and Reliability of Wearable Sensors for Continuous Postoperative Vital Signs Monitoring in Patients Recovering from Trauma Surgery
by Rianne van Melzen, Marjolein E. Haveman, Richte C. L. Schuurmann, Kai van Amsterdam, Mostafa El Moumni, Monique Tabak, Michel M. R. F. Struys and Jean-Paul P. M. de Vries
Sensors 2024, 24(19), 6379; https://doi.org/10.3390/s24196379 - 1 Oct 2024
Cited by 2 | Viewed by 2085
Abstract
(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO2). This study assessed the validity and reliability of two types of wearable sensors, [...] Read more.
(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO2). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO2, and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO2. Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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15 pages, 5368 KiB  
Article
Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning
by Mujeeb Rahman Kanhira Kadavath, Mohamed Nasor and Ahmed Imran
Sensors 2024, 24(16), 5231; https://doi.org/10.3390/s24165231 - 13 Aug 2024
Cited by 6 | Viewed by 4346
Abstract
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing [...] Read more.
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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13 pages, 2083 KiB  
Article
The Overlay, a New Solution for Volume Variations in the Residual Limb for Individuals with a Transtibial Amputation
by Pierre Badaire, Maxime T. Robert and Katia Turcot
Sensors 2024, 24(14), 4744; https://doi.org/10.3390/s24144744 - 22 Jul 2024
Viewed by 2516
Abstract
Background: The company Ethnocare has developed the Overlay, a new pneumatic solution for managing volumetric variations (VVs) of the residual limb (RL) in transtibial amputees (TTAs), which improves socket fitting. However, the impact of the Overlay during functional tasks and on the comfort [...] Read more.
Background: The company Ethnocare has developed the Overlay, a new pneumatic solution for managing volumetric variations (VVs) of the residual limb (RL) in transtibial amputees (TTAs), which improves socket fitting. However, the impact of the Overlay during functional tasks and on the comfort and pain felt in the RL is unknown. Methods: 8 TTAs participated in two evaluations, separated by two weeks. We measured compensatory strategies (CS) using spatio-temporal parameters and three-dimensional lower limb kinematics and kinetics during gait and sit-to-stand (STS) tasks. During each visit, the participant carried out our protocol while wearing the Overlay and prosthetic folds (PFs), the most common solution to VV. Between each task, comfort and pain felt were assessed using visual analog scales. Results: While walking, the cadence with the Overlay was 105 steps/min, while it was 101 steps/min with PFs (p = 0.021). During 35% and 55% of the STS cycle, less hip flexion was observed while wearing the Overlay compared to PFs (p = 0.004). We found asymmetry coefficients of 13.9% with the Overlay and 17% with PFs during the STS (p = 0.016) task. Pain (p = 0.031), comfort (p = 0.017), and satisfaction (p = 0.041) were better with the Overlay during the second visit. Conclusion: The Overlay’s impact is similar to PFs’ but provides less pain and better comfort. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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Review

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30 pages, 1550 KiB  
Review
The Potential of Wearable Sensors for Detecting Cognitive Rumination: A Scoping Review
by Vitica X. Arnold and Sean D. Young
Sensors 2025, 25(3), 654; https://doi.org/10.3390/s25030654 - 23 Jan 2025
Cited by 1 | Viewed by 1961
Abstract
Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators [...] Read more.
Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators associated with rumination. This scoping review investigates the current state of research on using wearable technology to detect cognitive rumination. Specifically, we examine the sensors and wearable devices used, physiological biomarkers measured, standard measures of rumination used, and the comparative validity of specific biomarkers in identifying cognitive rumination. The review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines on IEEE, Scopus, PubMed, and PsycInfo databases. Studies that used wearable devices to measure rumination-related physiological responses and biomarkers were included (n = 9); seven studies assessed one biomarker, and two studies assessed two biomarkers. Electrodermal Activity (EDA) sensors capturing skin conductance activity emerged as both the most prevalent sensor (n = 5) and the most comparatively valid biomarker for detecting cognitive rumination via wearable devices. Other commonly investigated biomarkers included electrical brain activity measured through Electroencephalogram (EEG) sensors (n = 2), Heart Rate Variability (HRV) measured using Electrocardiogram (ECG) sensors and heart rate fitness monitors (n = 2), muscle response measured through Electromyography (EMG) sensors (n = 1) and movement measured through an accelerometer (n = 1). The Empatica E4 and Empatica Embrace 2 wrist-worn devices were the most frequently used wearable (n = 3). The Rumination Response Scale (RRS), was the most widely used standard scale for assessing rumination. Experimental induction protocols, often adapted from Nolen-Hoeksema and Morrow’s 1993 rumination induction paradigm, were also widely used. In conclusion, the findings suggest that wearable technology offers promise in capturing real-time physiological responses associated with rumination. However, the field is still developing, and further research is needed to validate these findings and explore the impact of individual traits and contextual factors on the accuracy of rumination detection. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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